Modeling Expected Shortfall Using Tail Entropy
| Autor: | Miruna Mazurencu-Marinescu-Pele, Daniel Traian Pele, Emese Lazar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
050208 finance
Computer science measure of risk 05 social sciences Nonparametric statistics General Physics and Astronomy information entropy Standard measure 01 natural sciences Article tail risk 010104 statistics & probability Expected shortfall 0502 economics and business Econometrics JEL Classification Codes: C14 expected shortfall Entropy (information theory) C14 G10 Tail risk 0101 mathematics Smoothing C22 |
| Zdroj: | Entropy Volume 21 Issue 12 |
| ISSN: | 1099-4300 |
| Popis: | Given the recent replacement of value-at-risk as the regulatory standard measure of risk with expected shortfall (ES) undertaken by the Basel Committee on Banking Supervision, it is imperative that ES gives correct estimates for the value of expected levels of losses in crisis situations. However, the measurement of ES is affected by a lack of observations in the tail of the distribution. While kernel-based smoothing techniques can be used to partially circumvent this problem, in this paper we propose a simple nonparametric tail measure of risk based on information entropy and compare its backtesting performance with that of other standard ES models. |
| Databáze: | OpenAIRE |
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